{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "thorough-brake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'random_geom': (2000, 0.05, 'r_g'),\n",
       " 'random_geom_50': (50, 0.15, 'r_g'),\n",
       " 'Erdős–Rényi': (2000, 15000, 'gnm'),\n",
       " 'WS1': (2000, [15, 0], 'WS'),\n",
       " 'WS2': (2000, [15, 0.01], 'WS'),\n",
       " 'WS3': (2000, [15, 1], 'WS'),\n",
       " 'BA': (2000, 7, 'BA'),\n",
       " 'complete': (2000, 100500, 'complete')}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx\n",
    "import scipy.sparse as sprs\n",
    "import numpy as np\n",
    "#import scipy.sparse\n",
    "import scipy.sparse as sprs\n",
    "import copy \n",
    "import random as r_n\n",
    "import pylab\n",
    "from random import randint \n",
    "from matplotlib import mlab\n",
    "from scipy.stats import bernoulli\n",
    "from scipy.optimize import minimize\n",
    "import pandas as pd\n",
    "from scipy.sparse import csr_matrix, lil_matrix, hstack, vstack\n",
    "import random\n",
    "from tqdm.auto import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing \n",
    "from sklearn.preprocessing import normalize\n",
    "from random import choice\n",
    "from scipy.stats import binom\n",
    "import os\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from multiprocessing import Process\n",
    "from IPython.display import clear_output\n",
    "from statsmodels.stats.proportion import proportion_confint\n",
    "from IPython.display import clear_output\n",
    "import math\n",
    "from scipy import integrate\n",
    "import seaborn as sns\n",
    "from IPython.display import clear_output\n",
    "import time\n",
    "from sympy import Symbol\n",
    "from sympy import *\n",
    "from scipy.optimize import minimize, rosen, rosen_der\n",
    "import pickle\n",
    "\n",
    "from Model import experiment_one_matrix, experiment_two_matrices, save, load, save_noise\n",
    "\n",
    "topologies = {}\n",
    "\n",
    "topologies['random_geom'] = (2000, 0.05, 'r_g')\n",
    "topologies['random_geom_50'] = (50, 0.15, 'r_g')\n",
    "topologies['Erdős–Rényi'] = (2000, 15000, 'gnm')\n",
    "topologies['WS1'] = (2000, [15, 0], 'WS')\n",
    "topologies['WS2'] = (2000, [15, 0.01], 'WS')\n",
    "topologies['WS3'] = (2000, [15, 1], 'WS')\n",
    "topologies['BA'] = (2000, 7, 'BA')\n",
    "topologies['complete'] = (2000, 100500, 'complete')\n",
    "\n",
    "topologies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "centered-washington",
   "metadata": {},
   "source": [
    "# Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "minor-playing",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "N = 2000\n",
    "T = 500000\n",
    "t_freq = 25000\n",
    "\n",
    "#m = 10\n",
    "#possible_ops = np.arange(-1, 1+2/9, 2/9)[:-1]\n",
    "#th_value = 3\n",
    "\n",
    "#m = 10\n",
    "#possible_ops = np.arange(-1, 1+2/9, 2/9)[:-1]\n",
    "#th_value = 3\n",
    "\n",
    "#m = 3\n",
    "#possible_ops = np.array([-1, 0, 1])\n",
    "#th_value = 0\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "#load transition matrix\n",
    "transition_matrix = []\n",
    "for i in range(m):\n",
    "    transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "    #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in ['random_geom', 'WS1', 'WS2', 'WS3', 'BA', 'Erdős–Rényi']:\n",
    "\n",
    "        for in_ops_type in [('manual', [1, 0, 0]), \n",
    "                            ('manual', [0, 1, 0]), \n",
    "                            ('manual', [0, 0, 1]), \n",
    "                            ('uniform', )]:\n",
    "            \n",
    "            results  = experiment_one_matrix(N, \n",
    "                                             m, \n",
    "                                             transition_matrix, \n",
    "                                             in_ops_type, \n",
    "                                             possible_ops, \n",
    "                                             topologies, \n",
    "                                             topology_id, \n",
    "                                             selectivity_rate, \n",
    "                                             pers_rate, \n",
    "                                             th_value, \n",
    "                                             t_freq, \n",
    "                                             T)\n",
    "            \n",
    "            arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "            \n",
    "            main_folder = 'New_experiments'\n",
    "            \n",
    "            save(main_folder, arguments, results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "criminal-jamaica",
   "metadata": {},
   "source": [
    "# Noise in Model 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "joined-tuner",
   "metadata": {},
   "source": [
    "# Scenario 1"
   ]
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      "The notebook server will temporarily stop sending output\n",
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      "To change this limit, set the config variable\n",
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      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
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      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
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   "source": [
    "N, T, t_freq = 2000, 500000, 25000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "beta = 0\n",
    "phi = 0\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in [#'random_geom', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        #'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('uniform', ), \n",
    "                            #('manual', [0.1, 0.9]), \n",
    "                           ]:\n",
    "            \n",
    "            for alpha in [0, 0.005, 0.01, 0.015, 0.02, 0.025]:\n",
    "                for psi in [0, 0.01, 0.02, 0.03, 0.04, 0.049]:\n",
    "                    \n",
    "                    #...\n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "                    \n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "                    \n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "                    \n",
    "                    #transition_matrix[0][1, 0] -= beta\n",
    "                    #transition_matrix[0][1, 1] += beta\n",
    "\n",
    "                    #transition_matrix[1][0, 0] += beta\n",
    "                    #transition_matrix[1][0, 1] -= beta\n",
    "                    \n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "                        #print(transition_matrix[0])\n",
    "                        #print(transition_matrix[1])\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T             \n",
    "            \n",
    "            \n",
    "\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)\n",
    "                    "
   ]
  },
  {
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   "execution_count": null,
   "id": "prescription-comparative",
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      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
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   "source": [
    "N, T, t_freq = 2000, 1000000, 500000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "beta = 0\n",
    "phi = 0\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in [#'random_geom', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('uniform', ), \n",
    "                            #('manual', [1, 0, 0]), \n",
    "                            #('manual', [0, 1, 0]), \n",
    "                            #('manual', [0, 0, 1]), \n",
    "                           ]:\n",
    "            \n",
    "            for alpha in [0, 0.005, 0.01, 0.015, 0.02, 0.025]:\n",
    "                for psi in [0, 0.01, 0.02, 0.03, 0.04, 0.049]:\n",
    "                    \n",
    "                    #...\n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "                    \n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "                    \n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "                    \n",
    "                    #transition_matrix[0][1, 0] -= beta\n",
    "                    #transition_matrix[0][1, 1] += beta\n",
    "\n",
    "                    #transition_matrix[1][0, 0] += beta\n",
    "                    #transition_matrix[1][0, 1] -= beta\n",
    "                    \n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "                        #print(transition_matrix[0])\n",
    "                        #print(transition_matrix[1])\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T             \n",
    "            \n",
    "            \n",
    "\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "upper-reflection",
   "metadata": {},
   "outputs": [],
   "source": [
    "N, T, t_freq = 50, 100000, 25000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "beta = 0\n",
    "phi = 0\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in ['random_geom_50', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        #'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('manual', [0.1, 0.9]),\n",
    "                           ]:\n",
    "            \n",
    "            for alpha in [0, 0.005, 0.01, 0.015, 0.02, 0.025]:\n",
    "                for psi in [0, 0.01, 0.02, 0.03, 0.04, 0.049]:\n",
    "                    \n",
    "                    #...\n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "                    \n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "                    \n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "                    \n",
    "                    #transition_matrix[0][1, 0] -= beta\n",
    "                    #transition_matrix[0][1, 1] += beta\n",
    "\n",
    "                    #transition_matrix[1][0, 0] += beta\n",
    "                    #transition_matrix[1][0, 1] -= beta\n",
    "                    \n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "                        #print(transition_matrix[0])\n",
    "                        #print(transition_matrix[1])\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T             \n",
    "            \n",
    "            \n",
    "\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "military-annex",
   "metadata": {},
   "outputs": [],
   "source": [
    "N, T, t_freq = 50, 1000000, 500000\n",
    "\n",
    "N = 50\n",
    "T = 1000000\n",
    "t_freq = 500000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "beta = 0\n",
    "phi = 0\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in ['random_geom_50', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        #'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('manual', [0.1, 0.9]), \n",
    "                           ]:\n",
    "            \n",
    "            for alpha in [0, 0.005, 0.01, 0.015, 0.02, 0.025]:\n",
    "                for psi in [0, 0.01, 0.02, 0.03, 0.04, 0.049]:\n",
    "                    \n",
    "                    #...\n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "                    \n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "                    \n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "                    \n",
    "                    #transition_matrix[0][1, 0] -= beta\n",
    "                    #transition_matrix[0][1, 1] += beta\n",
    "\n",
    "                    #transition_matrix[1][0, 0] += beta\n",
    "                    #transition_matrix[1][0, 1] -= beta\n",
    "                    \n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "                        #print(transition_matrix[0])\n",
    "                        #print(transition_matrix[1])\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T             \n",
    "            \n",
    "            \n",
    "\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)\n",
    "                    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "representative-universe",
   "metadata": {},
   "source": [
    "# Scenario 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "specialized-spank",
   "metadata": {},
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     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-98dd351c865e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     69\u001b[0m                     \u001b[0mtransition_matrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransition_matrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m                     results  = experiment_one_matrix(N, \n\u001b[0m\u001b[1;32m     72\u001b[0m                                                      \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m                                                      \u001b[0mtransition_matrix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/project_opinion_dynamics/SI/SR2022/Model.py\u001b[0m in \u001b[0;36mexperiment_one_matrix\u001b[0;34m(N, m, transition_matrix, in_ops_type, possible_ops, topologies, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T)\u001b[0m\n\u001b[1;32m    335\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfrs\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m#if the set of friends is not empty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m             \u001b[0mfriend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m#take a random friend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m             \u001b[0mfriend_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfriend\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m#random friend's opinion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m             \u001b[0mfriend_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOps_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfriend\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#index of random friend's opinion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.randint\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_bounded_integers.pyx\u001b[0m in \u001b[0;36mnumpy.random._bounded_integers._rand_int64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mprod\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mprod\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m   3028\u001b[0m     \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3029\u001b[0m     \"\"\"\n\u001b[0;32m-> 3030\u001b[0;31m     return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n\u001b[0m\u001b[1;32m   3031\u001b[0m                           keepdims=keepdims, initial=initial, where=where)\n\u001b[1;32m   3032\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrapreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m     passkwargs = {k: v for k, v in kwargs.items()\n\u001b[0m\u001b[1;32m     72\u001b[0m                   if v is not np._NoValue}\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrapreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m     passkwargs = {k: v for k, v in kwargs.items()\n\u001b[0m\u001b[1;32m     72\u001b[0m                   if v is not np._NoValue}\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "N, T, t_freq = 2000, 500000, 25000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "\n",
    "alpha = 0.025\n",
    "psi = 0.049\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in ['random_geom', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        #'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('uniform', ), \n",
    "                           ]:\n",
    "            \n",
    "            for beta in [0.003, 0.006, 0.009, 0.012, 0.015, 0.018]:\n",
    "                for phi in [0]:\n",
    "                    \n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "\n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "\n",
    "\n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "\n",
    "                    if (phi == 0) & (beta == 0.018):\n",
    "                        transition_matrix[0][1, 0] = transition_matrix[1][0, 1]\n",
    "                        transition_matrix[0][1, 1] = transition_matrix[1][0, 0]\n",
    "                    else:\n",
    "                        transition_matrix[0][1, 0] -= beta\n",
    "                        transition_matrix[0][1, 1] += beta\n",
    "                        transition_matrix[1][0, 0] += phi\n",
    "                        transition_matrix[1][0, 1] -= phi            \n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "received-coordinator",
   "metadata": {},
   "outputs": [],
   "source": [
    "N, T, t_freq = 2000, 1000000, 500000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "alpha = 0.025\n",
    "psi = 0.049\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in [#'random_geom', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('uniform', ), \n",
    "                           ]:\n",
    "            \n",
    "            for beta in [0.003, 0.006, 0.009, 0.012, 0.015, 0.018]:\n",
    "                for phi in [0]:\n",
    "                    \n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "\n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "\n",
    "\n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "\n",
    "                    if (phi == 0) & (beta == 0.018):\n",
    "                        transition_matrix[0][1, 0] = transition_matrix[1][0, 1]\n",
    "                        transition_matrix[0][1, 1] = transition_matrix[1][0, 0]\n",
    "                    else:\n",
    "                        transition_matrix[0][1, 0] -= beta\n",
    "                        transition_matrix[0][1, 1] += beta\n",
    "                        transition_matrix[1][0, 0] += phi\n",
    "                        transition_matrix[1][0, 1] -= phi            \n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fleet-dietary",
   "metadata": {},
   "outputs": [],
   "source": [
    "N, T, t_freq = 50, 100000, 25000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "\n",
    "alpha = 0.025\n",
    "psi = 0.049\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in ['random_geom', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        #'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('manual', [0.1, 0.9]), \n",
    "                           ]:\n",
    "            \n",
    "            for beta in [0.003, 0.006, 0.009, 0.012, 0.015, 0.018]:\n",
    "                for phi in [0]:\n",
    "                    \n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "\n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "\n",
    "\n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "\n",
    "                    if (phi == 0) & (beta == 0.018):\n",
    "                        transition_matrix[0][1, 0] = transition_matrix[1][0, 1]\n",
    "                        transition_matrix[0][1, 1] = transition_matrix[1][0, 0]\n",
    "                    else:\n",
    "                        transition_matrix[0][1, 0] -= beta\n",
    "                        transition_matrix[0][1, 1] += beta\n",
    "                        transition_matrix[1][0, 0] += phi\n",
    "                        transition_matrix[1][0, 1] -= phi            \n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "former-recovery",
   "metadata": {},
   "outputs": [
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      "/home/mipt/.local/lib/python3.9/site-packages/networkx/algorithms/assortativity/correlation.py:287: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return (xy * (M - ab)).sum() / numpy.sqrt(vara * varb)\n"
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   "source": [
    "N, T, t_freq = 50, 1000000, 500000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "\n",
    "alpha = 0.025\n",
    "psi = 0.049\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in ['random_geom', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        #'BA', \n",
    "                        #'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('manual', [0.1, 0.9]), \n",
    "                           ]:\n",
    "            \n",
    "            for beta in [0.003, 0.006, 0.009, 0.012, 0.015, 0.018]:\n",
    "                for phi in [0]:\n",
    "                    \n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "\n",
    "                    if alpha != 0.025:\n",
    "                        transition_matrix[0][0, 0] += alpha\n",
    "                        transition_matrix[0][0, 1] -= alpha\n",
    "                    else:\n",
    "                        transition_matrix[0][0, 0] = 1\n",
    "                        transition_matrix[0][0, 1] = 0\n",
    "\n",
    "\n",
    "                    if psi != 0.049:\n",
    "                        transition_matrix[1][1, 0] -= psi\n",
    "                        transition_matrix[1][1, 1] += psi\n",
    "                    else:\n",
    "                        transition_matrix[1][1, 0] = 0\n",
    "                        transition_matrix[1][1, 1] = 1                       \n",
    "\n",
    "\n",
    "                    if (phi == 0) & (beta == 0.018):\n",
    "                        transition_matrix[0][1, 0] = transition_matrix[1][0, 1]\n",
    "                        transition_matrix[0][1, 1] = transition_matrix[1][0, 0]\n",
    "                    else:\n",
    "                        transition_matrix[0][1, 0] -= beta\n",
    "                        transition_matrix[0][1, 1] += beta\n",
    "                        transition_matrix[1][0, 0] += phi\n",
    "                        transition_matrix[1][0, 1] -= phi            \n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)                    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mysterious-scholarship",
   "metadata": {},
   "source": [
    "# Scenario 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "elect-juvenile",
   "metadata": {},
   "outputs": [],
   "source": [
    "N, T, t_freq = 2000, 500000, 25000\n",
    "\n",
    "m = 2\n",
    "possible_ops = np.array([-1, 1])\n",
    "th_value = 0\n",
    "\n",
    "selectivity_rate = -0.01  #no selectivity\n",
    "pers_rate = -0.01  #no personalization\n",
    "\n",
    "for experiment in range(20):\n",
    "\n",
    "    for topology_id in ['random_geom', \n",
    "                        #'WS1', \n",
    "                        #'WS2', \n",
    "                        #'WS3', \n",
    "                        #'BA', \n",
    "                        'Erdős–Rényi',\n",
    "                       ]:\n",
    "\n",
    "        for in_ops_type in [('uniform', ), \n",
    "                           ]:\n",
    "            \n",
    "            for alpha in [-0.02, -0.01, 0, 0.01, 0.02]:\n",
    "                for beta in [-0.02, -0.01, 0, 0.01, 0.02]:\n",
    "                    \n",
    "                    #load transition matrix\n",
    "                    transition_matrix = []\n",
    "                    for i in range(m):\n",
    "                        transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}.npy'))\n",
    "                        #transition_matrix.append(np.load(f'Transition matrices/Matrix_{m}_{i+1}_second_iteration.npy'))\n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "                    \n",
    "                    psi = alpha\n",
    "                    phi = beta\n",
    "\n",
    "                    transition_matrix[0][0, 0] += alpha\n",
    "                    transition_matrix[0][0, 1] -= alpha\n",
    "                    transition_matrix[1][1, 0] -= psi\n",
    "                    transition_matrix[1][1, 1] += psi\n",
    "\n",
    "                    transition_matrix[0][1, 0] -= beta\n",
    "                    transition_matrix[0][1, 1] += beta\n",
    "                    transition_matrix[1][0, 0] += phi\n",
    "                    transition_matrix[1][0, 1] -= phi            \n",
    "\n",
    "                    transition_matrix[0] = transition_matrix[0].T\n",
    "                    transition_matrix[1] = transition_matrix[1].T\n",
    "            \n",
    "                    results  = experiment_one_matrix(N, \n",
    "                                                     m, \n",
    "                                                     transition_matrix, \n",
    "                                                     in_ops_type, \n",
    "                                                     possible_ops, \n",
    "                                                     topologies, \n",
    "                                                     topology_id, \n",
    "                                                     selectivity_rate, \n",
    "                                                     pers_rate, \n",
    "                                                     th_value, \n",
    "                                                     t_freq, \n",
    "                                                     T)\n",
    "            \n",
    "                    arguments = N, m, in_ops_type, topology_id, selectivity_rate, pers_rate, th_value, t_freq, T\n",
    "\n",
    "                    main_folder = 'New_experiments_noise'\n",
    "\n",
    "                    save_noise(main_folder, arguments, results, alpha, beta, phi, psi)\n",
    "                    "
   ]
  }
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